> I was planning on using it for a design tool, but moved over to luna since it's comparable speeds and cost for a lot more intelligence.

Everyone should occasionally go back to the old models to see how much worse they were, like even a year ago you could generate results but they were typically full of bugs and you have to fix a non-insignificant amount of it all manually: https://blog.kronis.dev/blog/i-blew-through-24-million-token...

Admittedly that post was before agentic development truly took off and that 3k EUR figure when paying per API tokens would nowadays be closer to like 6k EUR for the volume of work I do, but still.

It's the same how Qwen 2.5 was pretty problematic for anything remotely serious, same with Qwen 3 Coder Next (80B), and at least the most recent versions are getting better but still not quite good enough in real world use cases outside of benchmarks. They've come a long way, regardless!

As MoE with 5B active parameters it's pretty fast. But you still need a lot of vRAM, or have to run small quantitations. Qwen models just gave you more bang for your buck, and the gap became worse with every qwen release

gpt-oss-120b is absolutely unusable over Cerebras because it fails to call tools half the time and just continues to think about what tool it'll call repeatedly. Like it says it'll call a tool and then it doesn't, and then it says it'll call the tool again and then it doesn't, and it just does that in a loop forever. It's awful. Also forgets to end the thinking block too. Even if the model itself was just-okay for its time, it's not good over Cerebras, even at 1000t/s+

You don't really need to train a 10T model to test cerebras against a 10T model. You can feed it an untrained (randomly initialized) model and benchmark it. Result will be gibberish but performance the same.

Mythos/Fable are around 10T:

> According to FT, industry estimates say Anthropic's most advanced Mythos 5 has about 8 trillion parameters and Fable 5 about 5 trillion

https://www.reuters.com/technology/bytedance-targets-mega-ai...

I believe this report has confused Opus (which is known to be around 5T) and Fable.

Other reports say 10T. See for example https://eu.36kr.com/en/p/3760679047267075?ref=explainx where Musk talks about the models being trained on Colossus2

pretty sure 10 trillion parameters is now the norm among closed ai labs, given that nvidia also references the same 10 trillion number for their nvl72 racks

It's rumored fable is around that 10T number

AMD did enter into an agreement to buy Taalas, which is speculated [1] will be used to augment their Helios offering.

1. https://www.youtube.com/watch?v=3MKRjt59hh4&pp=0gcJCRMMAYcqI...

They are Ex AMD employees. Nvidia tried back, but they rejected.

Is CUDA still a moat? Are we not at the point where frontier models can reimplement software stacks, given you throw enough tokens at the problem.

Rumors and allegations aren't worth much. Why don't they just tell us mere mortals?

save qwen3.8 27B which is outclassing much larger models and is in spitting distance of the top 10 in https://artificialanalysis.ai/models#intelligence

But that's supply and demand, not technology. Right now a lot more people want their inference than they can supply. as supply catches up in the next 5-10 years, the underlying tech at scale is probably cheaper than GPUs per token produced.

I love how instead of comparing a Ferrari (fast and expensive) to some average car (not fast, not expensive) to make your point..you went for public transport where your comparison cracks from multiple angles.

I thought your numbers must be wrong.

So I plugged 288 trillion tokens/month (OpenRouter's current rate), 500 billion MoE model average, and the math comes out to be around 620 B200 GPUs minimum.

So basically, OpenRouter's volume must be absolutely tiny compared to the volume hyperscalers are getting.

It is worth mentioning, the OpenRouter demand isn't static though. It has increased week on week since early 2026.

I was curious so I looked it up: looks like a GB300 NVL72 is about $4M. So $22B would buy you 5500 such racks, no?

not only that, but I was so happy with their GLM 4.8 that they got rid of yesterday :(

> save your money and retire.

Now that this hypothetical person has retired, what are they gonna do all day? Just sit on the beach and drink Mai Tais? If that's what they wanna do, sure, but nerds gonna nerd, and if I had that kind of money to retire on, I'd totally buy some ridiculously expensive AI box for fun.

Huh, why would it shatter if the power goes out?

For now you can rig an adapter to your nearest DC EV charging station, but make sure it's near a body of water for the cooling.

I guess we know why there's a fair bit of investment money going into small modular nuclear reactor startups now.

God, I was going to ask if this could be deployed in a standard existing datacenter, but I guess that answers that question.

I presume per rack?

Can you imagine something radiating that much energy into a space in your home?

Taalas will be one of the great disaster investments of the early AI era. It'll be a near total write-down.

The absolute worst market time to etch a model to a chip is right now (very rapid iteration). There is no scenario where they can keep up. The Taalas approach will be viewed as comically foolish within just a few years.

Cerebras will win in terms of approach.

It's 1998: hey, I can drastically speed up your web service, let's etch it right to silicon.

Whether it's a bubble or not depends on how much the demand for compute and the type of workload keeps growing, though.

If AI tends to be something used mainly in ideation and development, which is how a lot of people use it today, then once consumer hardware gets good enough you could see a bunch of the current data centre workloads move onto consumer devices.

But if AI starts being used more in repeatable, operational workloads I think it makes sense to have significant cloud infrastructure for it. TBH I haven't seen much of this, and I've been skeptical about people using agents for much of anything when it can be done with just software. But we are starting to see more of this kind of workload, like the taggable Claude in your slack etc that people seem to really love.

By the way, this is the same argument that Michael Burry used to short Nvidia.

He claims that GPU depreciation/obsoletion is much faster than hyperscalers are assuming because new chips will be much better. He's being proved wrong right now because H200 rental prices have been claiming for the last 8 month despite B200 having 10-20x better inference efficiency.[0]

The logic is fundamentally flawed in my opinion. Let's use future Nvidia chips being much better optimized for LLMs for example.

New Nvidia chips 10x better than H200 --> data centers buy a lot --> Nvidia profits a lot.

New Nvidia chips 10x better than H200 --> data centers don't buy --> no faster than expected obsoletion.

In other words, the very act of buying many new Nvidia GPUs would be the event that causes faster than expected obsoletion. Yet, if you don't buy those new Nvidia GPUs, then there is no faster than expected obsoletion.

We also live in a world where there is competition. If Amazon doesn't buy but Microsoft does, suddenly Microsoft can offer better $/token prices.

[0]https://inferencex.semianalysis.com/inference

I wonder: in world where inference is cheap, how many engineering agents that use simulation as their feedback we will use?

In the scenario, engineering everything becomes so easy - so why not optimize everything? every component, every product, every system?

And maybe llm's could invent. So even more to simulate. And simulation is inherently compute-heavy.

So unless there are some other bottlenecks, we'll use a lot of simulation servers.

True, I have to agree with you. The AI giants might be investing a huge amount of money in generation 1 technology. There might be a much better way to do it just around the corner. They might know this and thus the hurry to IPO.

A rough analogy would be if the first generation of ISP's spent billions on dial-up exchanges, when fibre could be invented next year.

On the plus side, lots of cheap servers to swoop up :)

Exactly right, and nVidia is protecting their moat through business practices rather than genuine product innovation.

By the time these gigawatt datacenters are done being built the hardware will be so far behind state of the art they may be mostly useless.

TPUs and ASICs run in data centers too. Your argument only holds true if there's some satisfied limit to demand for inference. If not, data centers will continue to spring up to host more and more agents. Even if agents were running on hardware and software as efficient as the human brain, its conceivable we want trillions of them running at any given time which would require data center scale.

I wonder what this looks like in 5 years... Will there be a massive push to repurpose these giant boxes into housing? Will they get turned back into the farm land from where they came? When a data center goes bust, what happens to the parts left behind?

Huh, why I'm not surprised that HN is full of opinions confidently stated without any numbers or resources to back up?

> built on both the insurmountable trillions of debt, and the assumption that only GPUs are all we need to continue scaling.

Insurmountable according to whom? And who assume that only GPUs are all we need to continue scaling? Google, Amazon, Microsoft, Meta and OpenAI, all have or plan custom non-GPU AI chips. Do they plan to use them not for scaling?

Depends on what you mean, they have more redundancy which means that the yield can be much higher.

There's a few more details at the bottom of this page: https://www.cerebras.ai/blog/introducing-cerebras-cs-4

44GB on-chip-sram * 3 chips. Per chip: 43.2 PB/s memory access + 53.5 PB/s on-chip fabric bandwidth + 2.4 Tbits/s "IO" bandwidth (I think that means their RoCE v2 RDMA over Ethernet interface).

I suspect there might be a certain amount of customization for how much RAM they attach when you order it.

You're thinking of Taalas.

That's unusually honest and the sharpest thing in this thread.

There’s been a spate of Reddit AI bots using all lower case in hopes of evading detection.

It’s still incredibly obvious.

« Make it look like human written »

...so, in the same ballapark as ddr5? :-)

The only way I see in which Nvidia can catch up is by buying Cerebras.

^

OpenAI is partnering with Cerebras while simultaneously investing in their own silicon play. Hedged bets.

After sitting thru their keynote today, it makes sense. The main throughput speedups they tout are an obvious evolution of the GPU that all companies will be building in the next year. Wafer-scale interconnected memory and compute is just going to beat out mountains of network cabling any day on both cost and performance metrics.

I mean the product is a server rack and while there's no advertised price I would assume it's six figures. So yes, an enterprise product.

But even an enterprise is going to care about the difference between "we can run the model we want with support from the manufacturer" and "we have to purchase the product, and then spend another 6 figure sum having developers port a recent model to the product to use it".